Introduction
In an era defined by rapid technological evolution and unprecedented environmental challenges, the World Meteorological Organization (WMO) has taken a decisive step toward integrating artificial intelligence (AI) across its global network. At its recent Council session, WMO unveiled an Action Plan on Artificial Intelligence designed to harness the power of AI for weather forecasting, climate monitoring, water management, and disaster risk reduction. This forward-looking strategy aims to accelerate scientific breakthroughs, enhance decision-making, and ensure that AI applications adhere to ethical and governance standards.
Structure
1. Why AI Matters for Weather and Climate Services
2. Core Components of the WMO AI Action Plan
3. Governance, Ethics, and Data Stewardship
4. Implementation, Partnerships, and Capacity Building
5. Anticipated Impacts and Next Steps
1. Why AI Matters for Weather and Climate Services
• Exploding Data Volumes: Modern meteorology relies on satellites, radars, ocean buoys, profiling balloons, and in situ sensors, generating petabytes of data daily. Processing this deluge demands intelligent algorithms capable of rapid pattern recognition.
• Complex Interactions: The Earth system is governed by nonlinear interactions among the atmosphere, oceans, land surface, cryosphere, and biosphere. AI methods—such as deep learning—can identify hidden relationships and refine model parameterizations more effectively than traditional techniques.
• Societal Imperatives: Accurate, timely forecasts and early warnings are critical to safeguarding lives and livelihoods. AI-driven analytics can shorten forecast times, boost spatial resolution, and deliver localized risk assessments for heatwaves, floods, droughts, and storms.
2. Core Components of the WMO AI Action Plan
The Action Plan outlines five strategic pillars to guide Members and the WMO Secretariat in adopting AI responsibly and effectively:
2.1 Data Management and Governance
• Standardized Metadata: Establish uniform descriptors for datasets to ensure interoperability across national meteorological and hydrological services.
• FAIR Principles: Promote data that are Findable, Accessible, Interoperable and Reusable, facilitating AI model training and validation.
2.2 Capacity Development
• Training Modules: Develop online courses, workshops and certification programmes focusing on machine learning fundamentals, climate informatics, and operational AI workflows.
• Fellowship and Exchange: Enable staff secondments between WMO Members and leading research institutions to transfer skills and best practices.
2.3 Standards, Methodologies, and Guidelines
• Model Evaluation Protocols: Define benchmarks for AI model accuracy, interpretability and reliability in forecasting applications.
• Ethical Frameworks: Integrate principles of transparency, accountability and fairness into AI development lifecycles.
2.4 Research, Innovation, and Partnerships
• Global AI Testbeds: Launch virtual laboratories to pilot novel AI algorithms on real-world meteorological, hydrological and climate datasets.
• Industry Collaboration: Forge partnerships with technology firms, academia and multilateral agencies to co-develop open-source tools and cloud-based computing platforms.
2.5 Communication, Outreach, and Stakeholder Engagement
• User-Centric Design: Co-create tailored AI-driven services with end users—from national disaster management agencies to agricultural planners—to ensure relevance and usability.
• Public Awareness: Disseminate success stories and lessons learned to foster trust and encourage adoption among WMO’s 193 Member States and Territories.
3. Governance, Ethics, and Data Stewardship
WMO recognizes that AI applications can amplify biases, compromise privacy, and yield opaque decision processes if left unchecked. The Action Plan therefore embeds cross-cutting governance measures:
• Ethical Oversight Committee: A high-level body to review AI proposals, assess potential risks, and certify compliance with UNESCO’s Recommendation on the Ethics of Artificial Intelligence.
• Algorithmic Transparency: Mandate that AI models used in operational forecasts provide explainable outputs, with diagnostic tools for anomaly detection and error attribution.
• Equity and Inclusion: Prioritize the development of AI solutions that serve low- and middle-income nations, closing the “digital divide” in climate and weather services.
4. Implementation, Partnerships, and Capacity Building
Operationalizing the AI Action Plan will involve a phased approach:
• Phase I – Needs Assessment (Year 1): Survey Members to map existing AI capabilities, data infrastructures and training gaps.
• Phase II – Pilot Projects and Standards Development (Years 2–3): Launch demonstration projects in diverse environments—urban heat monitoring, flash flood forecasting, agricultural advisories—while finalizing technical guidelines.
• Phase III – Scaling and Integration (Years 4–5): Roll out validated AI services across Regional Specialized Meteorological Centers, National Meteorological and Hydrological Services (NMHSs), and WMO-coordinated initiatives such as the Global Framework for Climate Services.
Key partners include the United Nations Development Programme (UNDP), World Bank’s Global Shield Fund, leading universities, and technology firms with high-performance computing infrastructure. WMO will also leverage its existing capacity-development programmes and regional training centres to disseminate AI curricula.
5. Anticipated Impacts and Next Steps
By embedding AI into core WMO activities, the organization expects to achieve:
• Quicker Forecast Cycles: Reduce end-to-end processing time for global numerical weather prediction from hours to minutes.
• Enhanced Resolution: Improve spatial granularity of forecasts from the current ~10 km scale to neighborhood- and farm-level detail.
• Risk Reduction: Increase lead times and accuracy for high-impact weather events, potentially saving thousands of lives and billions of dollars in economic losses annually.
Next steps involve establishing a WMO AI Office to coordinate working groups, securing funding for the Action Plan, and reporting progress to the next WMO Congress in 2027.
Three Key Takeaways
1. Holistic Strategy: WMO’s AI Action Plan combines technical standards, ethical guidelines, capacity building, and stakeholder engagement to ensure responsible AI adoption.
2. Global Collaboration: The initiative fosters partnerships among NMHSs, research institutions, technology companies, and humanitarian agencies, leveraging shared data and open-source tools.
3. Transformative Potential: AI-driven enhancements in forecast speed, resolution and risk assessment promise to bolster early warning systems and climate services, delivering concrete benefits to governments, businesses and communities.
Frequently Asked Questions (FAQ)
Q1: What is the timeline for rolling out the WMO AI Action Plan?
A1: The Plan will unfold over a five-year horizon. Year 1 focuses on a global needs assessment; Years 2–3 on pilot projects and standards development; Years 4–5 on scaling validated AI services across WMO’s network.
Q2: How will WMO ensure that AI tools are fair and transparent?
A2: WMO will set up an Ethical Oversight Committee and incorporate algorithmic transparency requirements. AI models must provide explainable outputs and undergo rigorous bias testing and peer review.
Q3: How can national meteorological services participate?
A3: NMHSs can engage through the Plan’s capacity-development programmes—online courses, fellowships and regional workshops—contribute to pilot projects, share data under FAIR principles, and join WMO’s AI testbeds.
